Defining your SEQopts()

Behind SEQopts()

SEQopts() provides the primary API to changing internal options of SEQuential(). While the documentation should go over their use, we will expand on them here in greater detail:

The options

General Options

Option Name	Description	Input Type	Example
bootstrap	Whether bootstrapping should take place	Boolean	TRUE
bootstrap.nboot	Number of bootstraps to run in addition to the full model	Integer	100L
bootstrap.sample	Subsample of data to use when bootstrapping	Float [0, 1]	0.8
calculate.var	Whether to calculate the robust standard errors for the outcome model	Boolean	FALSE
compevent	Column name for competing event variable	Char	“LTFU”
covariates	If provided, forces covariates for outcome models	Char	“X1+X2*X3+X4”
data.return	Whether to return expanded data as output	Boolean	TRUE
followup.class	Whether to expand followup values to an indicator matrix	Boolean	FALSE
followup.include	Whether to include ‘followup’ and ‘followup^2’ in outcome models	Boolean	TRUE
followup.max	Maximum value of followup per trial	Numeric	60
followup.min	Minimum value of followup per trial	Numeric	0
followup.spline	Whether to format followup values to a spline	Boolean	FALSE
hazard	Whether to estimate the hazard ratio	Boolean	FALSE
km.curves	Whether to estimate kaplan meier survival curves and data	Boolean	TRUE
multinomial	Whether to expect more than 2 treatment types	Boolean	FALSE
ncores	Number of cores to use in parallel multisession	Integer	8L
nthreads	Number of threads to use for data.table manipulations	Integer	16L
parallel	Whether the bootstrapping should be run in parallel	Boolean	TRUE
seed	Starting seed for random number generators	Integer	1636
selection.first_trial	Selects only the first eligible trial in the expanded dataset	Boolean	TRUE
selection.prob	Subsample of total IDs to select	Float [0, 1]	0.8
selection.random	Whether to randomly select IDs with replacement to run analysis	Boolean	FALSE
subgroup	Column name for subgroup analysis	Char	“sex”
survival.max	Maximum value for Risk/Survival curves	Numeric	60
treat.level	Treatment levels to compare	List	list(0, 1)
trial.include	Whether to include ‘trial’ and ‘trial^2’ in outcome models	Boolean	TRUE

Weighting Options

In general these only affect analytic methods of ‘dose-response’ and ‘censoring’. However, providing cense will allow ITT to be weighted in the case of a censoring variable like loss-to-followup.

Option Name	Description	Input Type	Example
cense	Column name for censoring variable	Char	“LTFU”
cense.denominator	If provided, forces denominator covariates for censoring models	Char	“A1+A2*A3+A4”
cense.eligible	Column name for indicator column defining which rows to use for censoring model	Char	“eligible_cense”
cense.numerator	If provided, forces numerator covariates for censoring models	Char	“Z1+Z2*Z3+Z4”
denominator	If provided, forces denominator covariates for weight models	Char	“C1+C2*C3+B4”
numerator	If provided, forces numerator covariates for weight models	Char	“B1+B2*B3+B4”
weight.eligible	List of columns for eligibility in weight models with certain treatment values	List	list(‘we1’, we2’)
weight.eligible_cols	List of column names for indicator columns defining which weights are eligible for weight models	List	list(“eligible1”, “eligible2”)
weight.lag_condition	Whether weights should be conditioned on treatment lag value	Boolean	TRUE
weight.lower	Lower truncation for weight values	Float	0.0
weight.p99	Whether to truncate weights at the 99% percentile	Boolean	TRUE
weight.preexpansion	Whether weighting should be done on pre-expanded data	Boolean	TRUE
weight.upper	Upper truncation for weight values	Float	1.0
weighted	Whether the analysis should be weighted	Boolean	TRUE

Plot Options (km.curves = TRUE)

All of these can be changed after SEQuential has finished analysis - if you forget to add them, no need to stop and restart. Additionally, SEQuential will output the data used for the plot if you would like to customize it yourself through your preferred plotting software.

Option Name	Description	Input Type	Example
plot.colors	Figure colors for output plot	List	list(“red”, “blue”)
plot.labels	Legend labels for output plot	List	list(“Tx 0”, “Tx 1”)
plot.subtitle	Subtitle for plot output	Char	“My Plot Subtitle”
plot.title	Title for plot output	Char	“My Plot Title”
plot.type	Type of output plot (“survival”, “risk”, “inc”)	Char	“survival”

Special Cases

Option Name	Description	Input Type	Example
deviation	Create switch based on deviation from column	Boolean	TRUE
deviation.col	Column name for deviation	Char	“deviation_var”
deviation.conditions	RHS evaluations of the same length as treat.levels	List	list(“>3”, “==1”)
deviation.excused	Whether deviations should be excused by deviation.excused_cols	Boolean	TRUE
deviation.excused_cols	Excused columns for deviation switches	List	list(“excuse1”, “excuse2”)
excused	When censoring, whether there is an excused condition	Boolean	TRUE
excused.cols	List of column names for treatment switch excuses	List	list(“excuse1”, “excuse2”)

Internal Options

These are internal options that change output name of covariates or the decomposition method when estimating coefficients.

Option Name	Description	Input Type	Example
fastglm.method	Method for decomposition by fastglm	Integer	3L
indicator.baseline	Identifier for baseline variables	Char	“_bas”
indicator.squared	Identifier for squared variables	Char	“_sq”